dineshresearch commited on
Commit
c199cb1
1 Parent(s): 1173f3e

Upload PPO LunarLander-v2 trained agent

Browse files
README.md ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: stable-baselines3
3
+ tags:
4
+ - LunarLander-v2
5
+ - deep-reinforcement-learning
6
+ - reinforcement-learning
7
+ - stable-baselines3
8
+ model-index:
9
+ - name: PPO
10
+ results:
11
+ - task:
12
+ type: reinforcement-learning
13
+ name: reinforcement-learning
14
+ dataset:
15
+ name: LunarLander-v2
16
+ type: LunarLander-v2
17
+ metrics:
18
+ - type: mean_reward
19
+ value: 258.82 +/- 16.61
20
+ name: mean_reward
21
+ verified: false
22
+ ---
23
+
24
+ # **PPO** Agent playing **LunarLander-v2**
25
+ This is a trained model of a **PPO** agent playing **LunarLander-v2**
26
+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
27
+
28
+ ## Usage (with Stable-baselines3)
29
+ TODO: Add your code
30
+
31
+
32
+ ```python
33
+ from stable_baselines3 import ...
34
+ from huggingface_sb3 import load_from_hub
35
+
36
+ ...
37
+ ```
config.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7efee01654c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7efee0165550>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7efee01655e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7efee0165670>", "_build": "<function ActorCriticPolicy._build at 0x7efee0165700>", "forward": "<function ActorCriticPolicy.forward at 0x7efee0165790>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7efee0165820>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7efee01658b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7efee0165940>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7efee01659d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7efee0165a60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7efee0165af0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7efee0163e00>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678695847393737060, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}
ppo-LunarLander-v2.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1dbbb4cc761d78513e7006568a35105a8606fb8d2d625471b7bfb7434d50c61d
3
+ size 147421
ppo-LunarLander-v2/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.7.0
ppo-LunarLander-v2/data ADDED
@@ -0,0 +1,95 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "policy_class": {
3
+ ":type:": "<class 'abc.ABCMeta'>",
4
+ ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
5
+ "__module__": "stable_baselines3.common.policies",
6
+ "__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
7
+ "__init__": "<function ActorCriticPolicy.__init__ at 0x7efee01654c0>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7efee0165550>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7efee01655e0>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7efee0165670>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7efee0165700>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7efee0165790>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7efee0165820>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7efee01658b0>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7efee0165940>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7efee01659d0>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7efee0165a60>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7efee0165af0>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc._abc_data object at 0x7efee0163e00>"
21
+ },
22
+ "verbose": 1,
23
+ "policy_kwargs": {},
24
+ "observation_space": {
25
+ ":type:": "<class 'gym.spaces.box.Box'>",
26
+ ":serialized:": "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",
27
+ "dtype": "float32",
28
+ "_shape": [
29
+ 8
30
+ ],
31
+ "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
32
+ "high": "[inf inf inf inf inf inf inf inf]",
33
+ "bounded_below": "[False False False False False False False False]",
34
+ "bounded_above": "[False False False False False False False False]",
35
+ "_np_random": null
36
+ },
37
+ "action_space": {
38
+ ":type:": "<class 'gym.spaces.discrete.Discrete'>",
39
+ ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
40
+ "n": 4,
41
+ "_shape": [],
42
+ "dtype": "int64",
43
+ "_np_random": null
44
+ },
45
+ "n_envs": 16,
46
+ "num_timesteps": 1015808,
47
+ "_total_timesteps": 1000000,
48
+ "_num_timesteps_at_start": 0,
49
+ "seed": null,
50
+ "action_noise": null,
51
+ "start_time": 1678695847393737060,
52
+ "learning_rate": 0.0003,
53
+ "tensorboard_log": null,
54
+ "lr_schedule": {
55
+ ":type:": "<class 'function'>",
56
+ ":serialized:": "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"
57
+ },
58
+ "_last_obs": {
59
+ ":type:": "<class 'numpy.ndarray'>",
60
+ ":serialized:": "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"
61
+ },
62
+ "_last_episode_starts": {
63
+ ":type:": "<class 'numpy.ndarray'>",
64
+ ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
65
+ },
66
+ "_last_original_obs": null,
67
+ "_episode_num": 0,
68
+ "use_sde": false,
69
+ "sde_sample_freq": -1,
70
+ "_current_progress_remaining": -0.015808000000000044,
71
+ "ep_info_buffer": {
72
+ ":type:": "<class 'collections.deque'>",
73
+ ":serialized:": "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"
74
+ },
75
+ "ep_success_buffer": {
76
+ ":type:": "<class 'collections.deque'>",
77
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
78
+ },
79
+ "_n_updates": 248,
80
+ "n_steps": 1024,
81
+ "gamma": 0.999,
82
+ "gae_lambda": 0.98,
83
+ "ent_coef": 0.01,
84
+ "vf_coef": 0.5,
85
+ "max_grad_norm": 0.5,
86
+ "batch_size": 64,
87
+ "n_epochs": 4,
88
+ "clip_range": {
89
+ ":type:": "<class 'function'>",
90
+ ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
91
+ },
92
+ "clip_range_vf": null,
93
+ "normalize_advantage": true,
94
+ "target_kl": null
95
+ }
ppo-LunarLander-v2/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7f36e01f5c333d1d1254874617db6f252259c67c9afc5d811488527314f66be4
3
+ size 87929
ppo-LunarLander-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:aba6d170522b23fe9ed6f14c4b28d6bbe0f5d3932468b26a3e0ae47f8149062c
3
+ size 43393
ppo-LunarLander-v2/pytorch_variables.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
3
+ size 431
ppo-LunarLander-v2/system_info.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ - OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
2
+ - Python: 3.9.16
3
+ - Stable-Baselines3: 1.7.0
4
+ - PyTorch: 1.13.1+cu116
5
+ - GPU Enabled: True
6
+ - Numpy: 1.22.4
7
+ - Gym: 0.21.0
replay.mp4 ADDED
Binary file (205 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 258.8174069590054, "std_reward": 16.607964181125464, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-13T08:46:03.483222"}